Syntactic Complexity in AI-Generated vs. Human-Authored Linguistic and Literary Texts

Abstract

This paper examines how the syntactic complexity of academic writing is affected mainly by the source of authorship (AI-generated or human) or by the genre of disciplinary writing (linguistic or literary). The primary purpose is to test the syntactic-complexity differences between these variables and to establish the degree of influence of genre conventions on structural variation. The importance of the research is that it adds to the existing discussions about AI as a phenomenon in academic writing and, specifically, whether AI-generated texts are capable of syntactically reproducing the specific norms of a specific discipline of writing. To this end, the comparative corpus-based design was used. The sample consisted of 20 introduction sections: equal numbers of linguistic and literary texts and equal numbers of human-authored AI-generated texts. The Second Language Syntactic Complexity Analyzer (L2SCA) extracts fourteen syntactic complexity measures, which include length of production unit, subordination, coordination and phrasal sophistication. The results indicate that the complexity of syntax is genre-based and not source-based. Although no differences were found to be constant in both AI-generated and human academic introductions in linguistic data, there were much higher levels of subordination in literary texts of human origin. In general, the discipline genre had a more significant effect on syntax variation than the authorship source. The paper suggests the implementation of genre-sensitive models to assess AI-written academic texts and recommends additional studies that would use a bigger sample and discourse analysis.